From 3a806a51e77660ca5be8029eafb9149773e879a5 Mon Sep 17 00:00:00 2001
From: Zhi Tian <tianzhi0549@163.com>
Date: Wed, 14 Aug 2019 17:25:31 +0930
Subject: [PATCH] add center sampling

---
 fcos_core/modeling/rpn/fcos/loss.py | 43 ++++++++++++++++++++++++++++-
 1 file changed, 42 insertions(+), 1 deletion(-)

diff --git a/fcos_core/modeling/rpn/fcos/loss.py b/fcos_core/modeling/rpn/fcos/loss.py
index 2af735c..bdbffd1 100644
--- a/fcos_core/modeling/rpn/fcos/loss.py
+++ b/fcos_core/modeling/rpn/fcos/loss.py
@@ -29,11 +29,44 @@ class FCOSLossComputation(object):
             cfg.MODEL.FCOS.LOSS_GAMMA,
             cfg.MODEL.FCOS.LOSS_ALPHA
         )
+        self.strides = cfg.MODEL.FCOS.FPN_STRIDES
         # we make use of IOU Loss for bounding boxes regression,
         # but we found that L1 in log scale can yield a similar performance
         self.box_reg_loss_func = IOULoss()
         self.centerness_loss_func = nn.BCEWithLogitsLoss()
 
+    def get_sample_region(self, gt, strides, num_points_per, gt_xs, gt_ys, radius=1):
+        num_gts = gt.shape[0]
+        K = len(gt_xs)
+        gt = gt[None].expand(K, num_gts, 4)
+        center_x = (gt[..., 0] + gt[..., 2]) / 2
+        center_y = (gt[..., 1] + gt[..., 3]) / 2
+        center_gt = gt.new_zeros(gt.shape)
+        # no gt
+        if center_x[..., 0].sum() == 0:
+            return gt_xs.new_zeros(gt_xs.shape, dtype=torch.uint8)
+        beg = 0
+        for level, n_p in enumerate(num_points_per):
+            end = beg + n_p
+            stride = strides[level] * radius
+            xmin = center_x[beg:end] - stride
+            ymin = center_y[beg:end] - stride
+            xmax = center_x[beg:end] + stride
+            ymax = center_y[beg:end] + stride
+            # limit sample region in gt
+            center_gt[beg:end, :, 0] = torch.where(xmin > gt[beg:end, :, 0], xmin, gt[beg:end, :, 0])
+            center_gt[beg:end, :, 1] = torch.where(ymin > gt[beg:end, :, 1], ymin, gt[beg:end, :, 1])
+            center_gt[beg:end, :, 2] = torch.where(xmax > gt[beg:end, :, 2], gt[beg:end, :, 2], xmax)
+            center_gt[beg:end, :, 3] = torch.where(ymax > gt[beg:end, :, 3], gt[beg:end, :, 3], ymax)
+            beg = end
+        left = gt_xs[:, None] - center_gt[..., 0]
+        right = center_gt[..., 2] - gt_xs[:, None]
+        top = gt_ys[:, None] - center_gt[..., 1]
+        bottom = center_gt[..., 3] - gt_ys[:, None]
+        center_bbox = torch.stack((left, top, right, bottom), -1)
+        inside_gt_bbox_mask = center_bbox.min(-1)[0] > 0
+        return inside_gt_bbox_mask
+
     def prepare_targets(self, points, targets):
         object_sizes_of_interest = [
             [-1, 64],
@@ -52,6 +85,7 @@ class FCOSLossComputation(object):
 
         expanded_object_sizes_of_interest = torch.cat(expanded_object_sizes_of_interest, dim=0)
         num_points_per_level = [len(points_per_level) for points_per_level in points]
+        self.num_points_per_level = num_points_per_level
         points_all_level = torch.cat(points, dim=0)
         labels, reg_targets = self.compute_targets_for_locations(
             points_all_level, targets, expanded_object_sizes_of_interest
@@ -91,7 +125,14 @@ class FCOSLossComputation(object):
             b = bboxes[:, 3][None] - ys[:, None]
             reg_targets_per_im = torch.stack([l, t, r, b], dim=2)
 
-            is_in_boxes = reg_targets_per_im.min(dim=2)[0] > 0
+            is_in_boxes = self.get_sample_region(
+                bboxes,
+                self.strides,
+                self.num_points_per_level,
+                xs,
+                ys,
+                radius=1.5
+            )
 
             max_reg_targets_per_im = reg_targets_per_im.max(dim=2)[0]
             # limit the regression range for each location
-- 
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